Building RAG Applications with Amazon Bedrock Knowledge Bases
Large language models can generate useful answers, but they do not automatically know the contents of your private documents, company policies, or project notes. This is where Retrieval-Augmented Generation (RAG) becomes useful.
Large language models can generate useful answers, but they do not automatically know the contents of your private documents, company policies, or project notes. This is where Retrieval-Augmented Generation (RAG) becomes useful.
Amazon Bedrock Knowledge Bases helps builders connect their data to foundation models and build applications that answer questions using relevant information from that data.
In this article, we’ll explore how Knowledge Bases works, why it is useful, and how to build a simple document-based question-answering application.
What is Amazon Bedrock Knowledge Bases?
Amazon Bedrock Knowledge Bases is a managed capability that helps you connect your data sources to generative AI applications.
Instead of manually building every step of a RAG pipeline, you can use Knowledge Bases to handle important parts of the process, including:
- Preparing and processing documents.
- Splitting documents into smaller chunks.
- Creating embeddings to represent the meaning of text.
- Retrieving relevant information when a user asks a question.
- Providing retrieved context to a foundation model to generate an answer.
The goal is to help the model answer questions based on your data rather than relying only on its general training knowledge.
How does the RAG workflow work?
Imagine building an assistant that answers questions about AWS documentation stored in your own knowledge base.
The workflow has two main stages.
1. Preparing your data
First, you provide documents from a supported data source, such as files stored in Amazon S3.
Knowledge Bases processes the documents and prepares them for retrieval. Depending on your configuration, this can involve splitting the content into chunks, generating embeddings, and storing those embeddings in a vector store.
An embedding represents the meaning of a piece of text as a numerical vector. This allows the system to search for content that is semantically related to a question, even when the wording is different.
2. Answering a user's question
When a user asks a question, the application retrieves relevant content from the knowledge base.
For example, a user might ask:
"What is the difference between an Amazon S3 bucket and an EC2 instance?"
The system searches for relevant information, retrieves matching document chunks, and sends that context along with the question to a foundation model.
The model then generates an answer using the retrieved information.
A simplified architecture looks like this:
Documents in Amazon S3 → Knowledge Base → Embeddings and vector store → Retrieve relevant content → Foundation model → AnswerA practical example: An AWS documentation assistant
Let's say you are building a small assistant for students who are learning AWS.
You have a collection of notes about Amazon EC2, Amazon S3, AWS Lambda, and Amazon VPC. Instead of asking students to search through multiple documents, you want them to ask questions in natural language.
Here is a simple implementation plan:
- Create an Amazon Bedrock Knowledge Base and configure a supported data source.
- Upload your AWS learning notes to Amazon S3.
- Configure the knowledge base to process the documents and create embeddings.
- Synchronize the data source so the knowledge base can retrieve the document content.
- Connect your application to the knowledge base and a suitable foundation model.
- Ask questions and inspect the retrieved sources along with the generated answers.
For example, when a student asks, "When should I use AWS Lambda instead of EC2?", the application can retrieve relevant sections from the notes and use them to generate a contextual explanation.
This makes the assistant useful for answering questions about a specific collection of learning materials.
Why use Knowledge Bases instead of building everything manually?
You can build a RAG pipeline yourself using document processing libraries, embedding models, vector databases, and a foundation model API.
However, that also means managing several separate components and integrating them correctly.
Amazon Bedrock Knowledge Bases can reduce the amount of custom infrastructure and integration code you need to write. This allows you to focus more on the application experience and the quality of your data.
It is still important to understand what happens behind the scenes. The quality of retrieved information depends on factors such as document structure, chunking, embedding configuration, and retrieval settings.
Important things to consider
A knowledge base does not automatically guarantee accurate answers.
If the documents are outdated, incomplete, or poorly organized, the retrieved context may not be useful. A model can also misunderstand the context it receives.
For a reliable application, consider:
- Keeping your documents updated.
- Testing questions that should have answers and questions that should not.
- Checking whether the retrieved chunks actually contain the information needed.
- Evaluating the answers for accuracy and relevance.
- Showing source references when your application supports them.
You should also consider access control. If your documents contain private information, make sure users can retrieve only the content they are authorized to access.
Final thoughts
Amazon Bedrock Knowledge Bases provides a practical way to build RAG applications without manually implementing every part of the retrieval pipeline.
For builders who are getting started with generative AI, a document-based assistant is a useful project because it introduces embeddings, semantic search, retrieval, and grounded generation in one workflow.
Start with a small collection of documents, test a few questions, and inspect the retrieved context before expanding the application.
Understanding how the retrieval process works is just as important as choosing the foundation model. A good RAG application depends on both the model and the information you provide to it.
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article